As the next-generation wireless communication system, Sixth-Generation (6G)
technologies are emerging, enabling various mobile edge networks that can
revolutionize wireless communication and connectivity. By integrating
Generative Artificial Intelligence (GAI) with mobile edge networks, generative
mobile edge networks possess immense potential to enhance the intelligence and
efficiency of wireless communication networks. In this article, we propose the
concept of generative mobile edge networks and overview widely adopted GAI
technologies and their applications in mobile edge networks. We then discuss
the potential challenges faced by generative mobile edge networks in
resource-constrained scenarios. To address these challenges, we develop a
universal resource-efficient generative incentive mechanism framework, in which
we design resource-efficient methods for network overhead reduction, formulate
appropriate incentive mechanisms for the resource allocation problem, and
utilize Generative Diffusion Models (GDMs) to find the optimal incentive
mechanism solutions. Furthermore, we conduct a case study on
resource-constrained mobile edge networks, employing model partition for
efficient AI task offloading and proposing a GDM-based Stackelberg model to
motivate edge devices to contribute computing resources for mobile edge
intelligence. Finally, we propose several open directions that could contribute
to the future popularity of generative mobile edge networks.

将生成式人工智能（GAI）与边缘计算网络结合，提出了生成式移动边缘网络的概念，并概述了广泛采用的 GAI 技术及其在移动边缘网络中的应用。在资源受限场景下，通过设计资源高效的方法、制定适当的激励机制以及利用生成扩散模型（GDMs）找到最优的激励机制解决方案，解决了生成式移动边缘网络面临的挑战。通过案例研究提出了一种模型分区的方法以实现智能任务卸载，并提出了基于 GDM 的 Stackelberg 模型来激励边缘设备为移动边缘智能提供计算资源。最后，提出了几个能够促进生成式移动边缘网络未来发展的方向。

6G 时代资源高效的移动边缘生成网络：基础、框架和案例研究

Resource-efficient Generative Mobile Edge Networks in 6G Era:  Fundamentals, Framework and Case Study

The requirement of wireless data demands is increasingly high as the
sixth-generation (6G) technology evolves. Reconfigurable intelligent surface
(RIS) is promisingly deemed to be one of 6G techniques for extending service
coverage, reducing power consumption, and enhancing spectral efficiency. In
this article, we have provided some fundamentals of RIS deployment in theory
and hardware perspectives as well as utilization of artificial intelligence
(AI) and machine learning. We conducted an intelligent deployment of RIS
(i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs
associated with an mmWave base station (BS) and a receiver. The RISs are
deployed on the AGV with configured incident/reflection angles. While, both the
mmWave BS and receiver are associated with an edge server monitoring downlink
packets for obtaining system throughput. We have designed a federated
multi-agent reinforcement learning scheme associated with several AGV-RIS
agents and sub-agents per AGV-RIS consisting of the deployment of position,
height, orientation and elevation angles. The experimental results presented
the stationary measurement in different aspects and scenarios. The i-Dris can
reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with
comparably low complexity as well as rapid deployment, which outperforms the
other existing works. At last, we highlight some opportunities and future
issues in leveraging RIS-empowered wireless communication networks.

本文提供了 RIS 在 6G 技术中的部署及其与人工智能和机器学习的利用的基础知识，并通过智能部署的 RIS 原型实验验证了其在增强系统吞吐量方面的性能优势和快速部署能力。